CN117556222A - Big data-based power station equipment real-time state evaluation and fault early warning method - Google Patents
Big data-based power station equipment real-time state evaluation and fault early warning method Download PDFInfo
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Abstract
The invention discloses a power station equipment real-time state evaluation and fault early warning method based on big data, which relates to the technical field of power station equipment monitoring and comprises the following steps: s1: collecting a plurality of parameters of the switch cabinet during operation, wherein the plurality of operation parameters comprise parameters of electrical equipment and environmental parameters in the switch cabinet; s2: and establishing a data analysis model for the parameters of the electrical equipment and the environmental parameters in the switch cabinet, and generating an influence coefficient. According to the invention, the electrical equipment parameters and the environmental parameters are collected when the switch cabinet operates, the data analysis model is built for the electrical equipment parameters and the environmental parameters, the influence coefficient is generated, the influence coefficient is compared with the threshold value, the high influence signal and the low influence signal are generated, if the operation state of the switch cabinet is poor, the high influence signal early warning prompt is generated, the working personnel is prompted to timely maintain and process the operation state problem of the switch cabinet, the switch cabinet is effectively prevented from being in the state for a long time, and the service life of the switch cabinet is effectively prolonged.
Description
Technical Field
The invention relates to the technical field of power station equipment monitoring, in particular to a power station equipment real-time state evaluation and fault early warning method based on big data.
Background
A power station refers to a facility that converts various energy sources (e.g., fire, water, wind, solar, etc.) into electrical energy, and stores, transports, and distributes the electrical energy. The power station is an important component of the power system and is a key link of electric energy production, transmission and distribution.
Power stations are used for many different types of power equipment, including generators, transformers, circuit breakers, switchgear, and automation control systems;
and (3) a generator: the main equipment for converting energy into electric energy is different in type and specification according to different energy sources and power generation modes;
a transformer: the device for electric energy transmission and distribution can transmit high voltage to a far place, and then the high voltage is reduced by a transformer so as to supply low voltage electric energy required by a user;
a circuit breaker: the switch and the protection for the power system can cut off the circuit when the circuit fails, and prevent the equipment from being damaged by overload or short circuit;
and a switch cabinet: the box body structure for installing and protecting the power equipment comprises various switches, protection and measurement devices and control and monitoring equipment, and the switch cabinet is commonly used for connecting and controlling important power equipment such as a generator, a transformer, a circuit breaker, a grounding switch and the like, so that the functions of protecting the equipment and guaranteeing the safe and stable operation of a power system are achieved;
an automation control system: the system is used for monitoring, controlling and managing the running state of the power system, and can realize remote control and intelligent management of the power equipment.
The prior art has the following defects: most of the switch cabinets in the prior art are regularly overhauled according to experience by technicians, the detection period is generally longer, when the running state of the switch cabinet is in a problem and needs to be timely maintained and processed, the service life of the switch cabinet is seriously influenced if the switch cabinet is in the state for a long time, and secondly, when the switch cabinet in a power distribution network power system is overhauled, the overhauling sequence cannot be determined, the switch cabinet with poor partial states can be overhauled after the switch cabinet is possibly caused, the damage of the switch cabinet can be accelerated, and therefore the maintenance efficiency of the switch cabinet is reduced.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The invention aims to provide a real-time state evaluation and fault early warning method for power station equipment based on big data.
In order to achieve the above object, the present invention provides the following technical solutions: a power station equipment real-time state evaluation and fault early warning method based on big data comprises the following steps:
s1: collecting a plurality of parameters of the switch cabinet during operation, wherein the plurality of operation parameters comprise parameters of electrical equipment and environmental parameters in the switch cabinet;
s2: establishing a data analysis model of electrical equipment parameters and environmental parameters in the switch cabinet to generate influence coefficients;
s3: comparing the influence coefficient with a threshold value to generate a high influence signal and a low influence signal, sending out an early warning prompt for the high influence signal and not sending out the early warning prompt for the low influence signal;
s4: establishing a data set for the influence coefficient corresponding to the low influence signal, analyzing the influence coefficient in the data set, generating a risk maintenance signal, and generating a maintenance coefficient through the risk maintenance signal;
s5: and sequencing the generated overhaul coefficients, and overhauling the switch cabinet according to the sequence.
Preferably, the electrical equipment parameters comprise electrical equipment current distribution coefficients and electrical equipment voltage distribution coefficients, the environmental parameters comprise temperature and humidity interference coefficients, and after acquisition, the electrical equipment current distribution coefficients, the electrical equipment voltage distribution coefficients and the temperature and humidity interference coefficients are respectively calibrated as。
Preferably, the current distribution coefficient of the electrical equipment, namely the distribution condition of the electrical equipment current in the switch cabinet, is obtained as follows:
calculating the distribution condition of the electric equipment current in the switch cabinet through the standard deviation of the electric equipment current, calibrating the standard deviation of the electric equipment current as s1, and calculating the standard deviation of the electric equipment current according to the formula:wherein->For the current value of the electrical equipment in the switchgear cabinet, < >>For the average value of the current values of the electrical equipment in the switch cabinet, N is the number of the electrical equipment in the switch cabinet, N is more than or equal to 2 and N is a positive integer, and the current distribution coefficient of the electrical equipment is obtained through the standard deviation value of the current of the electrical equipment ∈ ->。
Preferably, the distribution coefficient of the voltage of the electrical equipment in the switchgear, that is, the distribution condition of the voltage of the electrical equipment in the switchgear, is obtained as follows:
calculating the distribution condition of the electrical equipment voltage in the switch cabinet through the standard deviation of the electrical equipment voltage, calibrating the standard deviation of the electrical equipment voltage as s2, and calculating the standard deviation of the electrical equipment voltage according to the formula:wherein->For the voltage value of the electrical equipment in the switchgear cabinet, < >>For the average value of the voltage values of the electrical equipment in the switch cabinet, n is the number of the electrical equipment in the switch cabinet, n is more than or equal to 2 and n is a positive integer, and the electrical equipment voltage distribution coefficient ∈of the electrical equipment is obtained through the standard deviation value of the voltage of the electrical equipment>。
Preferably, the logic for acquiring the temperature and humidity interference coefficient is as follows:
setting a gradient range Wmin-Wmax for the temperature, acquiring the temperature value in the switch cabinet in real time, calibrating the temperature value in the switch cabinet to be W, if W is in the gradient range Wmin-Wmax, indicating that the temperature value in the switch cabinet is normal, if W is not in the gradient range Wmin-Wmax, indicating that the temperature value in the switch cabinet is abnormal, and calibrating the deviation value of the temperature in the switch cabinet to be W，The acquisition mode is as follows:
if W is less than Wmin, thenIs the absolute value of the difference between W and Wmin, if W is greater than Wmax, +.>Absolute value of the difference between W and Wmax;
setting a gradient range Smin-Smax for humidity, acquiring a humidity value in the switch cabinet in real time, calibrating the humidity value in the switch cabinet as S, if S is in the gradient range Smin-Smax, indicating that the humidity value in the switch cabinet is normal, if S is not in the gradient range Smin-Smax, indicating that the humidity value in the switch cabinet is abnormal, and calibrating a deviation value of the humidity in the switch cabinet as S，The acquisition mode is as follows:
if S is less than Smin, thenIs the absolute value of the difference between S and Smin, if S is greater than Smax, then +.>Absolute value of the difference between S and Smax;
the temperature and humidity interference coefficient is calculated through a formula, and the expression is as follows:the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->For the deviation value of the temperature in the switchgear cabinet, +.>For the deviation value of the humidity in the switch cabinet, t 1-t 2 are time periods when the temperature in the switch cabinet is not in the gradient range Wmin-Wmax, and t 3-t 4 are time periods when the humidity in the switch cabinet is not in the gradient range Wmin-Wmax.
Preferably, the current distribution coefficient of the electrical equipment is obtainedElectric device voltage distribution coefficient->Temperature and humidity interference coefficient->Then, a data analysis model is built, and influence coefficients are generated>The formula according to is:；
wherein Q is an error correction factor, the values of Q are 1.2348, h1, h2 and h3 are respectively preset proportional coefficients of current distribution coefficients, voltage distribution coefficients and temperature and humidity interference coefficients of the electrical equipment, and。
preferably, an influence coefficient will be generatedComparing with threshold BB1, if the influence coefficient is +>If the value is larger than or equal to the threshold value BB1, a high-influence signal early warning prompt is generated, prompting staff to maintain and process the running state problem of the switch cabinet in time, and if the influence coefficient is + ->And if the value is smaller than the threshold value BB1, generating a low-influence signal early warning prompt.
Preferably, when the switch cabinet is overhauled, a data set is established by using the influence coefficient corresponding to the low influence signal, and the data set is calibrated to be K, so thatI is the number of influence coefficients corresponding to the low influence signal, i=1, 2, 3, 4, i.e., E is equal to or greater than 2, and E is a positive integer.
Preferably, the influence coefficients in the data set are respectively matched with the thresholds BB1 and BB2 and BB3 are compared, wherein,if the influence coefficient->If the influence coefficient is less than the threshold BB1 and greater than or equal to the threshold BB2, generating a high-risk maintenance signal, and if the influence coefficient is + ->If the threshold value BB2 is smaller and larger than or equal to the threshold value BB3, a medium risk maintenance signal is generated, and if the influence coefficient is +>If the number of the high risk overhaul signals is smaller than the threshold value BB3, generating low risk overhaul signals, establishing a data set J from risk overhaul signals which are compared with the threshold values BB1, BB2 and BB3 in the data set, counting the number of the high risk overhaul signals, the number of the medium risk overhaul signals and the number of the low risk overhaul signals in the data set J, and calibrating the number of the high risk overhaul signals, the number of the medium risk overhaul signals and the number of the low risk overhaul signals as #, respectively>The number of the high-risk overhaul signals is +.>Number of medium risk service signals +.>And the number of low risk inspection signals +.>Establishing a data analysis model to generate an overhaul coefficient +.>The formula according to is:；
in the method, in the process of the invention,the preset proportional coefficients of the current distribution coefficient of the electrical equipment, the voltage distribution coefficient of the electrical equipment and the temperature and humidity interference coefficient are respectively, j represents the number of switch cabinets to be overhauled, and +.>。
Preferably, the maintenance coefficient of the switch cabinet is obtainedAfter that, the overhaul coefficient is->Sequencing in order of from big to small, giving priority to maintenance coefficient +.>And overhauling the switch cabinet with a large appearance value.
In the technical scheme, the invention has the technical effects and advantages that:
according to the invention, through collecting the electrical equipment parameters and the environmental parameters when the switch cabinet operates, the electrical equipment parameters and the environmental parameters are established into a data analysis model to generate the influence coefficient, the influence coefficient is compared with the threshold value to generate the high influence signal and the low influence signal, if the operation state of the switch cabinet is poorer, a high influence signal early warning prompt is generated to prompt a worker to timely maintain and treat the operation state problem of the switch cabinet, so that the switch cabinet is effectively prevented from being in the state for a long time, and the service life of the switch cabinet is effectively prolonged;
according to the invention, the data set is established through the influence coefficients corresponding to the low influence signals, the influence coefficients in the data set are analyzed, the risk maintenance signals are generated, the maintenance coefficients are generated through the risk maintenance signals, after the maintenance coefficients of the switch cabinets are obtained, the maintenance coefficients are ordered in sequence from large to small, and the switch cabinets with large values of the maintenance coefficients are maintained preferentially, so that the damage of the switch cabinets after the switch cabinets with poor states is prevented from being maintained and accelerated, and the maintenance efficiency of the switch cabinets is improved.
Drawings
For a clearer description of embodiments of the present application or of the solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments described in the present invention, and that other drawings may be obtained according to these drawings for a person skilled in the art.
FIG. 1 is a flow chart of a method for real-time state evaluation and fault early warning of power station equipment based on big data.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art.
The invention provides a power station equipment real-time state evaluation and fault early warning method based on big data as shown in figure 1, which comprises the following steps:
s1: collecting a plurality of parameters of the switch cabinet during operation, wherein the plurality of operation parameters comprise parameters of electrical equipment and environmental parameters in the switch cabinet;
the electrical equipment parameters comprise electrical equipment current distribution coefficients and electrical equipment voltage distribution coefficients, the environmental parameters comprise temperature and humidity interference coefficients, and after acquisition, the electrical equipment current distribution coefficients, the electrical equipment voltage distribution coefficients and the temperature and humidity interference coefficients are respectively calibrated as；
If the load current distribution of the equipment in the switchgear is not uniform, the following may occur:
local overload: some devices are overloaded with excessive load current, which leads to local overload of the circuit, overheat or even damage of the device, which may lead to failure of the device or safety accidents;
unbalanced voltage: uneven load current distribution in the switch cabinet can cause unbalanced current, so that unbalanced voltage is caused, equipment damage is possibly caused by the unbalanced voltage, maintenance cost is increased, and meanwhile, the stability and quality of a power grid are also influenced;
the energy consumption is increased: if the load current distribution in the switch cabinet is uneven, the load of some devices is light, and other devices are overloaded, so that the waste of electric energy and the increase of energy consumption are caused;
the service life of the equipment is shortened: the long-time local overload can cause the temperature in the equipment to rise, accelerate the aging of the equipment and shorten the service life of the equipment;
therefore, the current distribution condition of the electrical equipment in the switch cabinet is acquired when the switch cabinet operates, and the current value of the electrical equipment in the switch cabinet is acquired through the current sensor;
the distribution coefficient of the electric equipment current, namely the distribution condition of the electric equipment current in the switch cabinet, and the acquired logic is as follows:
calculating the distribution condition of the electric equipment current in the switch cabinet through the standard deviation of the electric equipment current, calibrating the standard deviation of the electric equipment current as s1, and calculating the standard deviation of the electric equipment current according to the formula:wherein->For the current value of the electrical equipment in the switchgear cabinet, < >>For the average value of the current values of the electrical equipment in the switch cabinet, N is the number of the electrical equipment in the switch cabinet, N is more than or equal to 2 and N is a positive integer, and the current distribution coefficient of the electrical equipment is obtained through the standard deviation value of the current of the electrical equipment ∈ ->；
If the load voltage distribution of the equipment in the switchgear is not uniform, the following may occur:
some devices are too high in voltage: when the voltage of some devices is too high, the devices can be unstable or even damaged in operation; for example, excessive motor voltage may cause motor overload or damage, while excessive low voltage devices such as lamps may cause lamp burning;
some devices are too low in voltage: when the voltage of some devices is too low, the devices cannot work normally or work efficiency is reduced; for example, too low a voltage of the motor may cause the motor to fail to start or the output power to decrease, while too low a voltage of low voltage devices such as lamps may cause the light to dim;
the energy consumption is increased: when the voltage distribution of the devices in the switch cabinet is uneven, the power of some devices is too high, and the power of other devices is too low, so that electric energy is wasted and energy consumption is increased;
the service life of the equipment is shortened: too high or too low a voltage may cause accelerated aging of the device, thereby shortening the lifetime of the device;
therefore, the voltage distribution condition of the electrical equipment in the switch cabinet is acquired when the switch cabinet operates, and the voltage value of the electrical equipment in the switch cabinet is acquired through a voltage sensor;
the distribution coefficient of the electrical equipment voltage, namely the distribution condition of the electrical equipment voltage in the switch cabinet, and the acquired logic is as follows:
calculating the distribution condition of the electrical equipment voltage in the switch cabinet through the standard deviation of the electrical equipment voltage, calibrating the standard deviation of the electrical equipment voltage as s2, and calculating the standard deviation of the electrical equipment voltage according to the formula:wherein->For the voltage value of the electrical equipment in the switchgear cabinet, < >>N is the average value of the voltage values of the electrical equipment in the switch cabinet, n is the number of the electrical equipment in the switch cabinet, n is more than or equal to 2 and n is a positive integer,obtaining an electrical device voltage distribution coefficient +.>；
When the humidity within the switchgear is too low, the following effects may occur on the electrical equipment:
reducing the insulation performance: electrical equipment within a switchgear often requires the use of insulating materials to isolate the different circuits, and when the humidity is too low, the insulating materials lose moisture, become dry, and are prone to cracking or splitting, thereby reducing their insulating properties and increasing the risk of equipment failure;
causing electrostatic problems: electrical equipment within a switchgear may malfunction due to electrostatic problems. When the humidity in the air is lower than a certain level, the problem of static electricity is more serious, because the dry air can cause static electricity to be more easily generated;
risk of increasing the lifetime of the device: too low a humidity may make mechanical parts inside the device more fragile and vulnerable, thus increasing the risk of failure of the device, and in addition, too low a humidity may make plastic and rubber parts inside some devices fragile, increasing the risk of ageing of the device;
the high humidity may have the following effects on the electrical equipment within the switchgear:
reducing the insulation performance: when the humidity in the switch cabinet is too high, moisture can permeate into equipment insulating materials, so that the insulating performance of the equipment is reduced, and the safety problems of easiness in wetting, easiness in occurrence of electric leakage and the like of the equipment are caused;
causing corrosion: the high humidity environment easily causes corrosion problems of electrical equipment in the switch cabinet, particularly oxidation reaction is generated after metal parts are easily wetted, and equipment is damaged or fails;
resulting in electrical leakage and shorting of electrical devices: the high humidity can increase the resistance inside the equipment, so that the equipment is easy to have the problems of electric leakage, short circuit and the like;
promote the growth of bacteria and mould: the high humidity environment easily causes the growth and propagation of microorganisms such as bacteria, mould and the like in the switch cabinet, and causes pollution to equipment, so that the maintenance cost is increased;
the high temperature may have the following effects on the electrical equipment within the switchgear:
accelerated equipment aging: the high temperature accelerates the aging of electrical equipment in the switch cabinet, so that the service life of the electrical equipment is shortened, and insulating materials and electronic components in the electrical equipment are easily damaged by heating, thereby influencing the normal operation of the equipment;
reducing the electrical performance of the device: the high temperature can reduce the electrical performance of the electrical equipment, for example, the insulating material of the transformer is easy to age and deteriorate, so that the insulating resistance is reduced, the withstand voltage is reduced and the like;
increasing the failure rate of the device: in a high-temperature environment, the failure rate of electrical equipment can be obviously increased, for example, a breaker in a switch cabinet is easy to generate failures such as blocking;
the energy consumption is increased: under a high-temperature environment, electrical equipment in the switch cabinet is easy to overheat, so that the energy consumption of the equipment is increased;
low temperatures may have the following effects on the electrical equipment within the switchgear:
reducing the insulation resistance of the device: the low temperature can cause the resistance of the insulation material of the electrical equipment in the switch cabinet to be reduced, thereby affecting the insulation performance of the equipment;
the power consumption of the device is increased: in a low-temperature environment, the resistance of the electrical equipment becomes large, so that the power consumption of the equipment is increased, and meanwhile, the loss of the equipment is also increased;
reducing the reliability of the device: in a low-temperature environment, the insulating material inside the electrical equipment is easy to generate cold brittleness and easy to generate faults such as cracking, thereby reducing the reliability of the equipment;
accelerated equipment aging: under the low-temperature environment, the mechanical properties of the electrical equipment are also affected, for example, the aging phenomenon of the wiring terminals, connectors and the like in the switch cabinet is easy to occur;
therefore, the temperature and humidity conditions in the switch cabinet are acquired when the switch cabinet operates and are acquired through the temperature sensor and the humidity sensor;
the logic for acquiring the temperature and humidity interference coefficient is as follows:
setting a gradient range Wmin-W for the temperaturemax, acquiring the temperature value in the switch cabinet in real time, calibrating the temperature value in the switch cabinet as W, if W is within the gradient range Wmin-Wmax, indicating that the temperature value in the switch cabinet is normal, if W is not within the gradient range Wmin-Wmax, indicating that the temperature value in the switch cabinet is abnormal, and calibrating the deviation value of the temperature in the switch cabinet as，The acquisition mode is as follows:
if W is less than Wmin, thenIs the absolute value of the difference between W and Wmin, if W is greater than Wmax, +.>Absolute value of the difference between W and Wmax;
the deviation value of the temperatureThe larger the temperature, the larger the influence of the temperature on the electrical equipment in the switch cabinet is, and the smaller the influence of the temperature on the electrical equipment in the switch cabinet is;
setting a gradient range Smin-Smax for humidity, acquiring a humidity value in the switch cabinet in real time, calibrating the humidity value in the switch cabinet as S, if S is in the gradient range Smin-Smax, indicating that the humidity value in the switch cabinet is normal, if S is not in the gradient range Smin-Smax, indicating that the humidity value in the switch cabinet is abnormal, and calibrating a deviation value of the humidity in the switch cabinet as S，The acquisition mode is as follows:
if S is less than Smin, thenIs the absolute value of the difference between S and Smin, if S is greater than Smax->Absolute value of the difference between S and Smax;
the deviation value of humidityThe larger the influence of the humidity on the electrical equipment in the switch cabinet is, and the smaller the influence of the humidity on the electrical equipment in the switch cabinet is;
the temperature and humidity interference coefficient is calculated through a formula, and the expression is as follows:the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->For the deviation value of the temperature in the switchgear cabinet, +.>For the deviation value of the humidity in the switch cabinet, t 1-t 2 are time periods when the temperature in the switch cabinet is not in the gradient range Wmin-Wmax, and t 3-t 4 are time periods when the humidity in the switch cabinet is not in the gradient range Wmin-Wmax;
s2: establishing a data analysis model of electrical equipment parameters and environmental parameters in the switch cabinet to generate influence coefficients;
obtaining the current distribution coefficient of the electrical equipmentElectric device voltage distribution coefficient->Temperature and humidity interference coefficient->Then, a data analysis model is built, and influence coefficients are generated>The formula according to is:；
wherein Q is an error correction factor, the values of Q are 1.2348, h1, h2 and h3 are respectively preset proportional coefficients of current distribution coefficients, voltage distribution coefficients and temperature and humidity interference coefficients of the electrical equipment, and；
as can be seen from the formula, the larger the current distribution coefficient of the electrical equipment is, the larger the voltage distribution coefficient of the electrical equipment is, and the larger the temperature and humidity interference coefficient is, namely the influence coefficientThe larger the expression value of the switch cabinet is, the worse the operation state of the switch cabinet is, the smaller the current distribution coefficient of the electrical equipment is, the smaller the voltage distribution coefficient of the electrical equipment is, the smaller the temperature and humidity interference coefficient is, namely the influence coefficient is->The smaller the expression value of (2) is, the better the running state of the switch cabinet is;
s3: comparing the influence coefficient with a threshold value to generate a high influence signal and a low influence signal, sending out an early warning prompt for the high influence signal and not sending out the early warning prompt for the low influence signal;
will generate an influence coefficientComparing with threshold BB1, if the influence coefficient is +>If the value is larger than or equal to the threshold BB1, which indicates that the operation state of the switch cabinet is relatively poor, a high-influence signal early warning prompt is generated to prompt a worker to timely maintain and process the operation state problem of the switch cabinet, effectively prevent the switch cabinet from being in the state for a long time, effectively prolong the service life of the switch cabinet, and if the influence coefficient is + +.>Less than threshold BB1, indicating a comparison of the operating states of the switch cabinetsIf the difference is found, generating a low-influence signal early warning prompt;
s4: establishing a data set for the influence coefficient corresponding to the low influence signal, analyzing the influence coefficient in the data set, generating a risk maintenance signal, and generating a maintenance coefficient through the risk maintenance signal;
when the switch cabinet is overhauled, a data set is established by the influence coefficient corresponding to the low influence signal, and the data set is calibrated to be K, so that the switch cabinet is convenient to installI is the number of influence coefficients corresponding to the low influence signal, i=1, 2, 3, 4, i.e., E is equal to or greater than 2, and E is a positive integer;
the influence coefficients in the data set are compared with thresholds BB1, BB2, BB3, respectively, wherein,if the influence coefficient->If the influence coefficient is less than the threshold BB1 and greater than or equal to the threshold BB2, generating a high-risk maintenance signal, and if the influence coefficient is + ->If the threshold value BB2 is smaller and larger than or equal to the threshold value BB3, a medium risk maintenance signal is generated, and if the influence coefficient is +>If the number of the high risk overhaul signals is smaller than the threshold value BB3, generating low risk overhaul signals, establishing a data set J from risk overhaul signals which are compared with the threshold values BB1, BB2 and BB3 in the data set, counting the number of the high risk overhaul signals, the number of the medium risk overhaul signals and the number of the low risk overhaul signals in the data set J, and calibrating the number of the high risk overhaul signals, the number of the medium risk overhaul signals and the number of the low risk overhaul signals as #, respectively>The number of the high-risk overhaul signals is +.>Number of medium risk service signals +.>And the number of low risk inspection signals +.>Establishing a data analysis model to generate an overhaul coefficient +.>The formula according to is:；
in the method, in the process of the invention,the preset proportional coefficients of the current distribution coefficient of the electrical equipment, the voltage distribution coefficient of the electrical equipment and the temperature and humidity interference coefficient are respectively, j represents the number of switch cabinets to be overhauled, and +.>；
From the formula, the maintenance coefficientThe larger the expression value of the switch cabinet is, the worse the operation state of the switch cabinet is, otherwise, the better the operation state of the switch cabinet is;
s5: sequencing the generated overhaul coefficients, and overhauling the switch cabinet according to the sequence;
obtaining the maintenance coefficient of the switch cabinetAfter that, the overhaul coefficient is->Sequencing in order of from big to small, giving priority to maintenance coefficient +.>The switch cabinet with a large appearance value is overhauled, so that the damage of the switch cabinet is accelerated after the switch cabinet with a poor part of states is overhauled, and the maintenance efficiency of the switch cabinet is improved;
according to the invention, through collecting the electrical equipment parameters and the environmental parameters when the switch cabinet operates, the electrical equipment parameters and the environmental parameters are established into a data analysis model to generate the influence coefficient, the influence coefficient is compared with the threshold value to generate the high influence signal and the low influence signal, if the operation state of the switch cabinet is poorer, a high influence signal early warning prompt is generated to prompt a worker to timely maintain and treat the operation state problem of the switch cabinet, so that the switch cabinet is effectively prevented from being in the state for a long time, and the service life of the switch cabinet is effectively prolonged;
according to the invention, the data set is established through the influence coefficients corresponding to the low influence signals, the influence coefficients in the data set are analyzed, the risk maintenance signals are generated, the maintenance coefficients are generated through the risk maintenance signals, after the maintenance coefficients of the switch cabinets are obtained, the maintenance coefficients are ordered in sequence from large to small, and the switch cabinets with large values of the maintenance coefficients are maintained preferentially, so that the damage of the switch cabinets after the switch cabinets with poor states is prevented from being maintained and accelerated, and the maintenance efficiency of the switch cabinets is improved.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided in this application, it should be understood that the disclosed methods may be implemented in other ways. For example, the embodiments described above are merely illustrative, e.g., the division of the elements is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. The real-time state evaluation and fault early warning method for the power station equipment based on the big data is characterized by comprising the following steps of:
s1: collecting a plurality of parameters of the switch cabinet during operation, wherein the plurality of operation parameters comprise parameters of electrical equipment and environmental parameters in the switch cabinet;
s2: establishing a data analysis model of electrical equipment parameters and environmental parameters in the switch cabinet to generate influence coefficients;
s3: comparing the influence coefficient with a threshold value to generate a high influence signal and a low influence signal, sending out an early warning prompt for the high influence signal and not sending out the early warning prompt for the low influence signal;
s4: establishing a data set for the influence coefficient corresponding to the low influence signal, analyzing the influence coefficient in the data set, generating a risk maintenance signal, and generating a maintenance coefficient through the risk maintenance signal;
s5: and sequencing the generated overhaul coefficients, and overhauling the switch cabinet according to the sequence.
2. The method for real-time state evaluation and fault early warning of power station equipment based on big data as claimed in claim 1, wherein the electrical equipment parameters comprise electrical equipment current distribution coefficient and electrical equipment voltage distribution coefficient, the environmental parameters comprise temperature and humidity interference coefficient, and after acquisition, the electrical equipment current distribution coefficient, the electrical equipment voltage distribution coefficient and the temperature and humidity interference coefficient are respectively calibrated as、/>、/>。
3. The method for real-time state evaluation and fault early warning of power station equipment based on big data according to claim 2, wherein the distribution coefficient of electric equipment current, namely the distribution condition of electric equipment current in a switch cabinet, is obtained by the following logic:
calculating the distribution condition of the electric equipment current in the switch cabinet through the standard deviation of the electric equipment current, calibrating the standard deviation of the electric equipment current as s1, and calculating the standard deviation of the electric equipment current according to the formula:wherein->For the current value of the electrical equipment in the switchgear cabinet, < >>For the average value of the current values of the electrical equipment in the switch cabinet, N is the number of the electrical equipment in the switch cabinet, N is more than or equal to 2 and N is a positive integer, and the current distribution coefficient of the electrical equipment is obtained through the standard deviation value of the current of the electrical equipment ∈ ->。
4. The method for real-time state evaluation and fault early warning of power station equipment based on big data according to claim 2, wherein the distribution coefficient of the voltage of the electrical equipment, namely the distribution condition of the voltage of the electrical equipment in the switch cabinet, is obtained by the following logic:
calculating the distribution condition of the electrical equipment voltage in the switch cabinet through the standard deviation of the electrical equipment voltage, calibrating the standard deviation of the electrical equipment voltage as s2, and calculating the standard deviation of the electrical equipment voltage according to the formula:wherein->For the voltage value of the electrical equipment in the switchgear cabinet, < >>Is a switchAverage value of voltage values of electrical equipment in the cabinet, n is the number of electrical equipment in the switch cabinet, n is more than or equal to 2 and n is a positive integer, and voltage distribution coefficient of the electrical equipment is obtained through standard deviation value of voltage of the electrical equipment>。
5. The method for real-time state evaluation and fault early warning of power station equipment based on big data according to claim 2, wherein the logic for acquiring the temperature and humidity interference coefficient is as follows:
setting a gradient range Wmin-Wmax for the temperature, acquiring the temperature value in the switch cabinet in real time, calibrating the temperature value in the switch cabinet to be W, if W is in the gradient range Wmin-Wmax, indicating that the temperature value in the switch cabinet is normal, if W is not in the gradient range Wmin-Wmax, indicating that the temperature value in the switch cabinet is abnormal, and calibrating the deviation value of the temperature in the switch cabinet to be W，/>The acquisition mode is as follows:
if W is less than Wmin, thenIs the absolute value of the difference between W and Wmin, if W is greater than Wmax, +.>Absolute value of the difference between W and Wmax;
setting a gradient range Smin-Smax for humidity, acquiring a humidity value in the switch cabinet in real time, calibrating the humidity value in the switch cabinet as S, if S is in the gradient range Smin-Smax, indicating that the humidity value in the switch cabinet is normal, if S is not in the gradient range Smin-Smax, indicating that the humidity value in the switch cabinet is abnormal, and calibrating a deviation value of the humidity in the switch cabinet as S，/>The acquisition mode is as follows:
if S is less than Smin, thenIs the absolute value of the difference between S and Smin, if S is greater than Smax, then +.>Absolute value of the difference between S and Smax;
the temperature and humidity interference coefficient is calculated through a formula, and the expression is as follows:the method comprises the steps of carrying out a first treatment on the surface of the In the method, in the process of the invention,for the deviation value of the temperature in the switchgear cabinet, +.>For the deviation value of the humidity in the switch cabinet, t 1-t 2 are time periods when the temperature in the switch cabinet is not in the gradient range Wmin-Wmax, and t 3-t 4 are time periods when the humidity in the switch cabinet is not in the gradient range Wmin-Wmax.
6. The method for real-time state evaluation and fault pre-warning of power station equipment based on big data as claimed in claim 2, wherein the current distribution coefficient of the electrical equipment is obtainedElectric device voltage distribution coefficient->Temperature and humidity interference coefficient->Then, a data analysis model is built, and influence coefficients are generated>The formula according to is:；
wherein Q is an error correction factor, h1, h2 and h3 are preset proportional coefficients of an electric device current distribution coefficient, an electric device voltage distribution coefficient and a temperature and humidity interference coefficient respectively, an。
7. The method for real-time state evaluation and fault pre-warning of power station equipment based on big data as claimed in claim 6, wherein the influence coefficient is generatedComparing with threshold BB1, if the influence coefficient is +>If the value is larger than or equal to the threshold value BB1, a high-influence signal early warning prompt is generated, prompting staff to maintain and process the running state problem of the switch cabinet in time, and if the influence coefficient is + ->And if the value is smaller than the threshold value BB1, generating a low-influence signal early warning prompt.
8. The real-time state evaluation and fault pre-warning method for power station equipment based on big data as claimed in claim 7, wherein when the switch cabinet is overhauled, a data set is built by using the influence coefficient corresponding to the low influence signal, and the data set is calibrated to be K, thenI is the number of influence coefficients corresponding to the low influence signal, i=1, 2, 3, 4, i.e., E is equal to or greater than 2, and E is a positive integer.
9. The real-time state evaluation and fault pre-warning method for power station equipment based on big data according to claim 8, wherein the influence coefficients in the data set are respectively compared with thresholds BB1, BB2 and BB3,if the influence coefficient->If the influence coefficient is less than the threshold BB1 and greater than or equal to the threshold BB2, generating a high-risk maintenance signal, and if the influence coefficient is + ->If the detection result is smaller than the threshold BB2 and larger than or equal to the threshold BB3, a medium risk maintenance signal is generated, and if the influence coefficient isIf the number of the high risk overhaul signals is smaller than the threshold value BB3, generating low risk overhaul signals, establishing a data set J from risk overhaul signals which are compared with the threshold values BB1, BB2 and BB3 in the data set, counting the number of the high risk overhaul signals, the number of the medium risk overhaul signals and the number of the low risk overhaul signals in the data set J, and calibrating the number of the high risk overhaul signals, the number of the medium risk overhaul signals and the number of the low risk overhaul signals as #, respectively>The number of the high-risk overhaul signalsNumber of medium risk service signals +.>And lowThe number of the equal risk overhaul signals>Establishing a data analysis model to generate an overhaul coefficient +.>The formula according to is: />；
In the method, in the process of the invention,the preset proportional coefficients of the current distribution coefficient of the electrical equipment, the voltage distribution coefficient of the electrical equipment and the temperature and humidity interference coefficient are respectively, j represents the number of switch cabinets to be overhauled, and +.>。
10. The method for real-time state evaluation and fault pre-warning of power station equipment based on big data as claimed in claim 9, wherein the maintenance coefficient of the switch cabinet is obtainedAfter that, the overhaul coefficient is->Sequencing in order of from big to small, giving priority to maintenance coefficient +.>And overhauling the switch cabinet with a large appearance value.
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| CN212969176U (en) * | 2020-08-24 | 2021-04-13 | 常州中能电力科技有限公司 | Switch cabinet temperature field monitoring and analyzing system |
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| CN212969176U (en) * | 2020-08-24 | 2021-04-13 | 常州中能电力科技有限公司 | Switch cabinet temperature field monitoring and analyzing system |
| CN113739846A (en) * | 2021-08-13 | 2021-12-03 | 秦皇岛龙鼎电气有限公司 | Switch cabinet based on multi-parameter detection and universal monitoring and management system |
| CN114994441A (en) * | 2022-06-06 | 2022-09-02 | 重庆伏特猫科技有限公司 | Intelligent electric power efficiency monitoring device |
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